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Record W2010692024 · doi:10.1002/aic.11403

CaCo<sub>3</sub> supported Co‐Fe catalysts for carbon nanotube synthesis in fluidized bed reactors

2008· article· en· W2010692024 on OpenAlexfundno aff
Chee Howe See, Andrew T. Harris

Bibliographic record

VenueAIChE Journal · 2008
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsnot available
FundersUniversity of SydneyOntario Ministry of Natural Resources and Forestry
KeywordsCatalysisFluidized bedCarbon nanotubeCobaltThermogravimetric analysisChemical engineeringCarbon fibersFluidizationMaterials scienceCitric acidSubstrate (aquarium)HydrolysisYield (engineering)ChemistryNanotechnologyMetallurgyOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract The synthesis of carbon nanotubes (CNTs) on a cobalt‐iron (Co‐Fe) catalyst supported on a calcium carbonate (CaCO 3 ) substrate, contained within a fluidized bed was investigated for the first time. CaCO 3 supported catalysts, prepared using traditional wet impregnation techniques result in a soft and sticky powder requiring special arrangements to ensure satisfactory fluidization. A modified method developed using citric acid complex resulted in CaCO 3 supported catalysts that can be classically fluidized without special arrangements. The issue of Fe 3+ (aq) hydrolysis above its critical pH of ∼3.5 was also addressed. A carbon yield consistently greater than 10 times the weight of the catalyst metal loading was obtained. Thermogravimetric analysis coupled with mass spectrometry showed a single peak centered on 575°C, with no peaks below 400°C, indicating that an insignificant quantity of amorphous carbon was present. Transmission electron microscopy verified that the products contained a large proportion of CNTs. © 2008 American Institute of Chemical Engineers AIChE J 2008

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.255
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations26
Published2008
Admission routes1
Has abstractyes

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